“Yes, obviously,” you say. Stand by … because here’s where it gets interesting.

“When I systematically test, I can boost two or all three variables. That’s how I can dramatically increase revenue because lifts in multiple variables compound results.”

The process is simple: Measure what you do, interpret the results, and form a hypothesis to do better next time. Repeat.

Why repeat? Check out the change in revenue: it’s more than the sum of the increases…

It can’t be that simple, can it? What’s really going on here?

Because testing and optimization validate your hypotheses, you get insights. Insights are learnings that can be repeated from one experiment to another.

In other words, your lift from the last experiment becomes your baseline in the next.

So, when you apply the optimization process to two (or all three) FCORM variables, you don’t just increase revenue. You dramatically increase revenue.

We affectionately call this win-stacking. Think of it like compound interest—you get paid on principal and the “interest” of all your previous tests.

This is how you stop running campaigns as isolated events (like a series of coin tosses) and start building a systematic program that delivers predictable revenue.

How to get more YESes (and revenue): the conversion heuristic

So, you’ve done the FCORM self-analysis by now (right?), and you know which variable, once optimized, will yield the biggest impact.

How do you influence that variable?

Here’s the tool we use to engineer a test designed to lift that variable. We can use it both to evaluate the status quo and form specific hypotheses.

If the FCORM is GDP, the conversion heuristic helps you fix the sector that’s dragging down gross productivity.

Or here’s a sports analogy: If FCORM is the scoreboard, the conversion heuristic is the playbook to optimize your offense (or defense, if the scoreboard indicates that’s the problem).

We run our baseline through this formula to form a hypothesis for how to boost the FCORM variable we’re targeting. We also use it as a sort of checklist to craft the creative elements within the campaign. In other words, it helps us choose which levers to pull first.

Here’s an example: Let’s suppose the FCORM analysis says you should focus on Conversion Rate first. You run a few tests and validate a treatment that increases conversion.

Now, focus on another variable, say Traffic, but still apply your Conversion Rate learning (which is now part of the baseline/control). Now you’re sending more traffic to a page that converts at a higher rate than before.

More people arriving and a higher percentage of those people converting means the net impact is more than the sum of the two lifts.

Stacking incremental targeted wins like that is how you grow reliably and predictably.

Plus, there’s no reason to stop after one lift, either. Continue testing your Conversion Rate, for instance, even if you’ve made progress already.

Remember the inverted funnel? Prospects aren’t falling in, they’re falling out. To get to “yes” they must climb the sides of the inverted funnel–a series of “micro-yeses.”

If there are multiple micro-yeses feeding one FCORM variable (and there usually are), make sure you apply the conversion heuristic to all of them. Neglecting just one can scuttle results for an entire campaign. Of course, expected lifts that don’t materialize are also useful as a signal that some micro-yes

Of course, expected lifts that don’t materialize are also useful as a signal that some micro-yes is not optimized. But they’re costly.

Here’s a simple example. Suppose you’re targeting Traffic in a broadcast email campaign to a donation page. Traffic isn’t optimized once email open is lifted. You also need the click-through to landing page. Make sure you’re identifying and measuring each micro-yes and running tests to optimize each one.

This happens a lot with page views, for example. A big increase in page views looks great in your Analytics dashboard. It doesn’t mean anything (except that you wasted a bunch of money and effort) if all that traffic abandons your funnel at the first micro-yes.

The FCORM keeps you focused on the variables that matter. If you neglect a micro-yes in the conversion pathway of each variable, you won’t see the FCORM lift you expect. Find the bottleneck. Then test into a complete process that lifts the FCORM variable before you move on to the next one.

3 steps to starting your own optimization program

By now you should have a pretty good idea why optimization is transformational. Here’s a quick review of how to do it:

Use the FCORM audit to see where you should focus first. The FCORM is the simple formula that determines your revenue (and identifies what’s holding you back). It is the top measure for online fundraising success and the program-level metrics you should constantly track. It’s your online fundraising dashboard.

Next, use the MECLABS conversion heuristic to devise a test to influence whichever FCORM variable you are targeting first.

Apply this learning through the entire conversion pathway for each FCORM variable. Identify each micro-yes related to that variable throughout the entire conversion process—and optimize it.

Finally, don’t stop at a win—keep testing. Re-run the FCORM audit and repeat the process for the next variable.

Remember, the real power of optimization is compounding wins through the FCORM formula.

What’s better than optimizing your funnel?

What could be better than systematically boosting revenue by optimizing your entire funnel?

Here’s a bold claim: The non-revenue benefits of optimization are both larger and more impactful than the revenue benefits.

That’s a crazy thing to say for a man whose livelihood is based on optimizing online fundraising revenue. But it’s true. I’ve seen it time and again.